Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations18290
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory176.0 B

Variable types

DateTime1
TimeSeries18
Categorical2

Timeseries statistics

Number of series18
Time series length18290
Starting point2022-09-01 00:00:00
Ending point2024-10-03 00:00:00
Period1 hour and 5.05 seconds
2025-02-02T22:15:01.149745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:15:02.569152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Alerts

fltValPronostico is non stationary Non stationary
iNumDia is non stationary Non stationary
iNumSemana is non stationary Non stationary
iNumDiaSemana is non stationary Non stationary
iNumMes is non stationary Non stationary
iCodDia is non stationary Non stationary
iCodHora is non stationary Non stationary
iNumHora is non stationary Non stationary
fltTemp is non stationary Non stationary
fltProbabilidadLluvia is non stationary Non stationary
fltHumedadRelativa is non stationary Non stationary
fltVelocidadViento is non stationary Non stationary
fltCoberturaNubes is non stationary Non stationary
fltIndiceUV is non stationary Non stationary
iCodDirViento is non stationary Non stationary
fltVelocidadRafaga is non stationary Non stationary
fltDPT is non stationary Non stationary
fltValPronostico is seasonal Seasonal
iNumDia is seasonal Seasonal
iNumSemana is seasonal Seasonal
iNumDiaSemana is seasonal Seasonal
iNumMes is seasonal Seasonal
iNumHora is seasonal Seasonal
fltTemp is seasonal Seasonal
fltProbabilidadLluvia is seasonal Seasonal
fltHumedadRelativa is seasonal Seasonal
fltVelocidadViento is seasonal Seasonal
fltCoberturaNubes is seasonal Seasonal
fltIndiceUV is seasonal Seasonal
iCodDirViento is seasonal Seasonal
fltVelocidadRafaga is seasonal Seasonal
fltDPT is seasonal Seasonal
fltPrecipitacion is highly skewed (γ1 = 34.19037814) Skewed
iCodDia is uniformly distributed Uniform
iCodHora is uniformly distributed Uniform
iCodHora has unique values Unique
fltValPronostico has 8419 (46.0%) zeros Zeros
fltProbabilidadLluvia has 11516 (63.0%) zeros Zeros
fltCoberturaNubes has 2064 (11.3%) zeros Zeros
fltIndiceUV has 10148 (55.5%) zeros Zeros
fltPrecipitacion has 18241 (99.7%) zeros Zeros
fltDPT has 338 (1.8%) zeros Zeros

Reproduction

Analysis started2025-02-03 04:13:03.873513
Analysis finished2025-02-03 04:14:59.971609
Duration1 minute and 56.1 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

dtHora
Date

Distinct18289
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size285.8 KiB
Minimum2022-09-01 00:00:00
Maximum2024-10-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-02T22:15:03.352175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:15:03.583845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fltValPronostico
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct6576
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9392.4704
Minimum0
Maximum30000
Zeros8419
Zeros (%)46.0%
Memory size285.8 KiB
2025-02-02T22:15:03.937469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median532
Q321829.75
95-th percentile28373.1
Maximum30000
Range30000
Interquartile range (IQR)21829.75

Descriptive statistics

Standard deviation11398.664
Coefficient of variation (CV)1.213596
Kurtosis-1.3253582
Mean9392.4704
Median Absolute Deviation (MAD)532
Skewness0.64150474
Sum1.7178828 × 108
Variance1.2992955 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.493492496 × 10-9
2025-02-02T22:15:04.436637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:05.796686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:06.480911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8419
46.0%
30000 98
 
0.5%
28500 24
 
0.1%
27041 20
 
0.1%
27861 19
 
0.1%
24 19
 
0.1%
27000 19
 
0.1%
25263 19
 
0.1%
27441 18
 
0.1%
25500 18
 
0.1%
Other values (6566) 9617
52.6%
ValueCountFrequency (%)
0 8419
46.0%
1 7
 
< 0.1%
2 15
 
0.1%
3 9
 
< 0.1%
4 15
 
0.1%
5 8
 
< 0.1%
6 7
 
< 0.1%
6.151029118 1
 
< 0.1%
6.584958692 1
 
< 0.1%
7 5
 
< 0.1%
ValueCountFrequency (%)
30000 98
0.5%
29968 1
 
< 0.1%
29964 1
 
< 0.1%
29951 3
 
< 0.1%
29950 1
 
< 0.1%
29946 5
 
< 0.1%
29936 5
 
< 0.1%
29935 5
 
< 0.1%
29933 12
 
0.1%
29930 1
 
< 0.1%
2025-02-02T22:15:04.968805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iNumDia
Numeric time series

Non stationary  Seasonal 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.681137
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:07.181356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8182863
Coefficient of variation (CV)0.56234992
Kurtosis-1.1961921
Mean15.681137
Median Absolute Deviation (MAD)8
Skewness0.010111028
Sum286808
Variance77.762174
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.058995907 × 10-13
2025-02-02T22:15:07.682281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
2025-02-02T22:15:08.778963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:09.050211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 624
 
3.4%
2 624
 
3.4%
3 601
 
3.3%
4 600
 
3.3%
5 600
 
3.3%
6 600
 
3.3%
7 600
 
3.3%
8 600
 
3.3%
9 600
 
3.3%
10 600
 
3.3%
Other values (21) 12241
66.9%
ValueCountFrequency (%)
1 624
3.4%
2 624
3.4%
3 601
3.3%
4 600
3.3%
5 600
3.3%
6 600
3.3%
7 600
3.3%
8 600
3.3%
9 600
3.3%
10 600
3.3%
ValueCountFrequency (%)
31 336
1.8%
30 553
3.0%
29 576
3.1%
28 600
3.3%
27 600
3.3%
26 600
3.3%
25 599
3.3%
24 577
3.2%
23 600
3.3%
22 600
3.3%
2025-02-02T22:15:08.051339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iNumAnio
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.8 KiB
2023
8736 
2024
6625 
2022
2929 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters73160
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2023 8736
47.8%
2024 6625
36.2%
2022 2929
 
16.0%

Length

2025-02-02T22:15:09.365366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T22:15:09.600269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2023 8736
47.8%
2024 6625
36.2%
2022 2929
 
16.0%

Most occurring characters

ValueCountFrequency (%)
2 39509
54.0%
0 18290
25.0%
3 8736
 
11.9%
4 6625
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73160
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 39509
54.0%
0 18290
25.0%
3 8736
 
11.9%
4 6625
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 73160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 39509
54.0%
0 18290
25.0%
3 8736
 
11.9%
4 6625
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 39509
54.0%
0 18290
25.0%
3 8736
 
11.9%
4 6625
 
9.1%

iNumSemana
Numeric time series

Non stationary  Seasonal 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.233953
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:09.932643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median28
Q340
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.038464
Coefficient of variation (CV)0.5521954
Kurtosis-1.1921697
Mean27.233953
Median Absolute Deviation (MAD)13
Skewness-0.06527347
Sum498109
Variance226.15539
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.133156825
2025-02-02T22:15:10.249066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:11.000960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:11.216626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
37 504
 
2.8%
39 504
 
2.8%
38 504
 
2.8%
40 433
 
2.4%
36 408
 
2.2%
45 337
 
1.8%
41 336
 
1.8%
43 336
 
1.8%
42 336
 
1.8%
44 336
 
1.8%
Other values (43) 14256
77.9%
ValueCountFrequency (%)
1 312
1.7%
2 336
1.8%
3 336
1.8%
4 336
1.8%
5 336
1.8%
6 336
1.8%
7 336
1.8%
8 336
1.8%
9 336
1.8%
10 336
1.8%
ValueCountFrequency (%)
53 192
1.0%
52 336
1.8%
51 336
1.8%
50 336
1.8%
49 336
1.8%
48 336
1.8%
47 336
1.8%
46 336
1.8%
45 337
1.8%
44 336
1.8%
2025-02-02T22:15:10.534748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iNumDiaSemana
Numeric time series

Non stationary  Seasonal 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0024604
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:11.601056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0001215
Coefficient of variation (CV)0.499723
Kurtosis-1.2491195
Mean4.0024604
Median Absolute Deviation (MAD)2
Skewness-0.0026040829
Sum73205
Variance4.000486
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2025-02-02T22:15:11.879643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
2025-02-02T22:15:12.746552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:12.964762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5 2617
14.3%
1 2617
14.3%
6 2616
14.3%
7 2616
14.3%
4 2616
14.3%
3 2615
14.3%
2 2593
14.2%
ValueCountFrequency (%)
1 2617
14.3%
2 2593
14.2%
3 2615
14.3%
4 2616
14.3%
5 2617
14.3%
6 2616
14.3%
7 2616
14.3%
ValueCountFrequency (%)
7 2616
14.3%
6 2616
14.3%
5 2617
14.3%
4 2616
14.3%
3 2615
14.3%
2 2593
14.2%
1 2617
14.3%
2025-02-02T22:15:12.116696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iNumMes
Numeric time series

Non stationary  Seasonal 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6263532
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:13.341289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4173533
Coefficient of variation (CV)0.51572158
Kurtosis-1.1977835
Mean6.6263532
Median Absolute Deviation (MAD)3
Skewness-0.083638309
Sum121196
Variance11.678304
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1623478077
2025-02-02T22:15:13.562885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
2025-02-02T22:15:14.247181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:14.496199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
9 2160
11.8%
10 1538
8.4%
12 1488
8.1%
1 1488
8.1%
8 1488
8.1%
3 1488
8.1%
5 1488
8.1%
7 1464
8.0%
11 1440
7.9%
4 1440
7.9%
Other values (2) 2808
15.4%
ValueCountFrequency (%)
1 1488
8.1%
2 1368
7.5%
3 1488
8.1%
4 1440
7.9%
5 1488
8.1%
6 1440
7.9%
7 1464
8.0%
8 1488
8.1%
9 2160
11.8%
10 1538
8.4%
ValueCountFrequency (%)
12 1488
8.1%
11 1440
7.9%
10 1538
8.4%
9 2160
11.8%
8 1488
8.1%
7 1464
8.0%
6 1440
7.9%
5 1488
8.1%
4 1440
7.9%
3 1488
8.1%
2025-02-02T22:15:13.813491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iCodDia
Numeric time series

Non stationary  Uniform 

Distinct763
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6834.0337
Minimum6452
Maximum7215
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:14.948621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6452
5-th percentile6491
Q16643
median6834
Q37025
95-th percentile7177
Maximum7215
Range763
Interquartile range (IQR)382

Descriptive statistics

Standard deviation220.41935
Coefficient of variation (CV)0.032253184
Kurtosis-1.2021457
Mean6834.0337
Median Absolute Deviation (MAD)191
Skewness-0.00089317689
Sum1.2499448 × 108
Variance48584.688
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.9332778153
2025-02-02T22:15:15.345712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:16.395334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:16.611922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
6512 25
 
0.1%
6462 24
 
0.1%
7215 24
 
0.1%
7214 24
 
0.1%
6453 24
 
0.1%
6454 24
 
0.1%
6455 24
 
0.1%
6456 24
 
0.1%
6457 24
 
0.1%
6458 24
 
0.1%
Other values (753) 18049
98.7%
ValueCountFrequency (%)
6452 1
 
< 0.1%
6453 24
0.1%
6454 24
0.1%
6455 24
0.1%
6456 24
0.1%
6457 24
0.1%
6458 24
0.1%
6459 24
0.1%
6460 24
0.1%
6461 24
0.1%
ValueCountFrequency (%)
7215 24
0.1%
7214 24
0.1%
7213 24
0.1%
7212 24
0.1%
7211 24
0.1%
7210 24
0.1%
7209 24
0.1%
7208 24
0.1%
7207 24
0.1%
7206 24
0.1%
2025-02-02T22:15:15.695664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iCodHora
Numeric time series

Non stationary  Uniform  Unique 

Distinct18290
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164005.23
Minimum154847
Maximum173160
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:17.097146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum154847
5-th percentile155761.45
Q1159419.25
median164015.5
Q3168587.75
95-th percentile172245.55
Maximum173160
Range18313
Interquartile range (IQR)9168.5

Descriptive statistics

Standard deviation5290.1925
Coefficient of variation (CV)0.032256242
Kurtosis-1.2020702
Mean164005.23
Median Absolute Deviation (MAD)4584.5
Skewness-0.00096126801
Sum2.9996557 × 109
Variance27986136
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9322074419
2025-02-02T22:15:17.398189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:18.111502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:18.344687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
173160 1
 
< 0.1%
154847 1
 
< 0.1%
173144 1
 
< 0.1%
173143 1
 
< 0.1%
173142 1
 
< 0.1%
173141 1
 
< 0.1%
173140 1
 
< 0.1%
173139 1
 
< 0.1%
173138 1
 
< 0.1%
173137 1
 
< 0.1%
Other values (18280) 18280
99.9%
ValueCountFrequency (%)
154847 1
< 0.1%
154848 1
< 0.1%
154849 1
< 0.1%
154850 1
< 0.1%
154851 1
< 0.1%
154852 1
< 0.1%
154853 1
< 0.1%
154854 1
< 0.1%
154855 1
< 0.1%
154856 1
< 0.1%
ValueCountFrequency (%)
173160 1
< 0.1%
173159 1
< 0.1%
173158 1
< 0.1%
173157 1
< 0.1%
173156 1
< 0.1%
173155 1
< 0.1%
173154 1
< 0.1%
173153 1
< 0.1%
173152 1
< 0.1%
173151 1
< 0.1%
2025-02-02T22:15:17.662770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iNumHora
Numeric time series

Non stationary  Seasonal 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.501312
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:18.765443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile23
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9231365
Coefficient of variation (CV)0.55379279
Kurtosis-1.2041633
Mean12.501312
Median Absolute Deviation (MAD)6
Skewness3.8301477 × 10-6
Sum228649
Variance47.929819
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2025-02-02T22:15:19.239178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
2025-02-02T22:15:20.060912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:20.496973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
24 763
 
4.2%
1 762
 
4.2%
2 762
 
4.2%
3 762
 
4.2%
4 762
 
4.2%
5 762
 
4.2%
6 762
 
4.2%
7 762
 
4.2%
8 762
 
4.2%
9 762
 
4.2%
Other values (15) 10669
58.3%
ValueCountFrequency (%)
1 762
4.2%
2 762
4.2%
3 762
4.2%
4 762
4.2%
5 762
4.2%
6 762
4.2%
7 762
4.2%
8 762
4.2%
9 762
4.2%
10 762
4.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
24 763
4.2%
23 762
4.2%
22 762
4.2%
21 762
4.2%
20 762
4.2%
19 762
4.2%
18 762
4.2%
17 762
4.2%
16 762
4.2%
2025-02-02T22:15:19.526830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltTemp
Numeric time series

Non stationary  Seasonal 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.456698
Minimum0
Maximum39
Zeros3
Zeros (%)< 0.1%
Memory size285.8 KiB
2025-02-02T22:15:21.138698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q115
median19
Q324
95-th percentile31
Maximum39
Range39
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.6365386
Coefficient of variation (CV)0.34109275
Kurtosis-0.29225007
Mean19.456698
Median Absolute Deviation (MAD)4
Skewness0.15677598
Sum355863
Variance44.043644
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.390621417 × 10-6
2025-02-02T22:15:21.711902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
2025-02-02T22:15:23.146371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:23.622086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
17 1333
 
7.3%
18 1327
 
7.3%
19 1184
 
6.5%
16 1158
 
6.3%
20 972
 
5.3%
21 877
 
4.8%
22 833
 
4.6%
15 787
 
4.3%
23 762
 
4.2%
24 752
 
4.1%
Other values (30) 8305
45.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 12
 
0.1%
2 10
 
0.1%
3 27
 
0.1%
4 56
 
0.3%
5 107
 
0.6%
6 139
0.8%
7 210
1.1%
8 302
1.7%
9 330
1.8%
ValueCountFrequency (%)
39 3
 
< 0.1%
38 17
 
0.1%
37 51
 
0.3%
36 73
 
0.4%
35 78
 
0.4%
34 119
 
0.7%
33 176
1.0%
32 234
1.3%
31 310
1.7%
30 380
2.1%
2025-02-02T22:15:22.223563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltProbabilidadLluvia
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct80
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9125752
Minimum0
Maximum90
Zeros11516
Zeros (%)63.0%
Memory size285.8 KiB
2025-02-02T22:15:24.736444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile44
Maximum90
Range90
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.166862
Coefficient of variation (CV)2.226925
Kurtosis7.668959
Mean5.9125752
Median Absolute Deviation (MAD)0
Skewness2.8277654
Sum108141
Variance173.36624
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.787078073 × 10-19
2025-02-02T22:15:25.621767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:27.842947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:28.632379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 11516
63.0%
7 1582
 
8.6%
1 690
 
3.8%
20 576
 
3.1%
6 434
 
2.4%
5 427
 
2.3%
2 384
 
2.1%
4 295
 
1.6%
3 285
 
1.6%
47 252
 
1.4%
Other values (70) 1849
 
10.1%
ValueCountFrequency (%)
0 11516
63.0%
1 690
 
3.8%
2 384
 
2.1%
3 285
 
1.6%
4 295
 
1.6%
5 427
 
2.3%
6 434
 
2.4%
7 1582
 
8.6%
8 40
 
0.2%
9 26
 
0.1%
ValueCountFrequency (%)
90 7
< 0.1%
88 1
 
< 0.1%
84 1
 
< 0.1%
80 7
< 0.1%
79 3
< 0.1%
75 7
< 0.1%
74 2
 
< 0.1%
73 6
< 0.1%
72 4
< 0.1%
71 7
< 0.1%
2025-02-02T22:15:26.445941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltHumedadRelativa
Numeric time series

Non stationary  Seasonal 

Distinct98
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.879224
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:29.726240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile14
Q137
median62
Q383
95-th percentile94
Maximum100
Range97
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.112205
Coefficient of variation (CV)0.44348759
Kurtosis-1.1479525
Mean58.879224
Median Absolute Deviation (MAD)22
Skewness-0.28285682
Sum1076901
Variance681.84724
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.818442855 × 10-13
2025-02-02T22:15:30.425993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:31.677204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:32.226686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
85 394
 
2.2%
87 392
 
2.1%
88 373
 
2.0%
86 362
 
2.0%
84 354
 
1.9%
89 339
 
1.9%
82 324
 
1.8%
90 323
 
1.8%
83 309
 
1.7%
81 298
 
1.6%
Other values (88) 14822
81.0%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 11
 
0.1%
5 21
 
0.1%
6 44
 
0.2%
7 53
 
0.3%
8 65
0.4%
9 104
0.6%
10 112
0.6%
11 139
0.8%
12 123
0.7%
ValueCountFrequency (%)
100 278
1.5%
99 76
 
0.4%
98 69
 
0.4%
97 114
0.6%
96 139
0.8%
95 177
1.0%
94 193
1.1%
93 223
1.2%
92 254
1.4%
91 262
1.4%
2025-02-02T22:15:30.819295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltVelocidadViento
Numeric time series

Non stationary  Seasonal 

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.577255
Minimum0
Maximum57
Zeros4
Zeros (%)< 0.1%
Memory size285.8 KiB
2025-02-02T22:15:32.926851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q17
median11
Q317
95-th percentile24
Maximum57
Range57
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.4797591
Coefficient of variation (CV)0.5151966
Kurtosis2.0333614
Mean12.577255
Median Absolute Deviation (MAD)4
Skewness1.211072
Sum230038
Variance41.987279
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.336509368 × 10-19
2025-02-02T22:15:33.367626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
2025-02-02T22:15:34.408597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:34.812654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
7 2994
16.4%
9 2656
14.5%
6 2196
12.0%
11 2036
11.1%
13 1595
8.7%
15 1380
7.5%
17 1152
 
6.3%
19 987
 
5.4%
20 836
 
4.6%
22 691
 
3.8%
Other values (31) 1767
9.7%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 8
 
< 0.1%
2 33
 
0.2%
3 18
 
0.1%
4 344
 
1.9%
5 19
 
0.1%
6 2196
12.0%
7 2994
16.4%
8 15
 
0.1%
9 2656
14.5%
ValueCountFrequency (%)
57 1
 
< 0.1%
56 2
 
< 0.1%
54 1
 
< 0.1%
52 4
 
< 0.1%
50 3
 
< 0.1%
48 2
 
< 0.1%
46 2
 
< 0.1%
44 14
0.1%
43 8
< 0.1%
41 18
0.1%
2025-02-02T22:15:33.726187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltCoberturaNubes
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.308201
Minimum0
Maximum100
Zeros2064
Zeros (%)11.3%
Memory size285.8 KiB
2025-02-02T22:15:35.428749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median48
Q392
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)80

Descriptive statistics

Standard deviation37.865105
Coefficient of variation (CV)0.75266267
Kurtosis-1.5710348
Mean50.308201
Median Absolute Deviation (MAD)39
Skewness0.026686913
Sum920137
Variance1433.7662
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.650480397 × 10-22
2025-02-02T22:15:35.942960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:37.393869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:38.243466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
100 2315
 
12.7%
0 2064
 
11.3%
99 727
 
4.0%
1 356
 
1.9%
98 355
 
1.9%
97 315
 
1.7%
2 301
 
1.6%
70 296
 
1.6%
3 254
 
1.4%
96 231
 
1.3%
Other values (91) 11076
60.6%
ValueCountFrequency (%)
0 2064
11.3%
1 356
 
1.9%
2 301
 
1.6%
3 254
 
1.4%
4 214
 
1.2%
5 199
 
1.1%
6 192
 
1.0%
7 192
 
1.0%
8 171
 
0.9%
9 184
 
1.0%
ValueCountFrequency (%)
100 2315
12.7%
99 727
 
4.0%
98 355
 
1.9%
97 315
 
1.7%
96 231
 
1.3%
95 181
 
1.0%
94 188
 
1.0%
93 161
 
0.9%
92 151
 
0.8%
91 120
 
0.7%
2025-02-02T22:15:36.443666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltIndiceUV
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9763095
Minimum0
Maximum14
Zeros10148
Zeros (%)55.5%
Memory size285.8 KiB
2025-02-02T22:15:39.226573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.181351
Coefficient of variation (CV)1.6097434
Kurtosis3.0343912
Mean1.9763095
Median Absolute Deviation (MAD)0
Skewness1.9037097
Sum36146.7
Variance10.120994
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.626831966 × 10-7
2025-02-02T22:15:40.164856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:41.688554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:42.005032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 10148
55.5%
1 1525
 
8.3%
2 1515
 
8.3%
3 1159
 
6.3%
5 957
 
5.2%
4 780
 
4.3%
12 440
 
2.4%
9 422
 
2.3%
6 321
 
1.8%
8 252
 
1.4%
Other values (50) 771
 
4.2%
ValueCountFrequency (%)
0 10148
55.5%
0.1 5
 
< 0.1%
0.2 8
 
< 0.1%
0.3 5
 
< 0.1%
0.4 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 3
 
< 0.1%
0.7 1
 
< 0.1%
0.8 2
 
< 0.1%
0.9 1
 
< 0.1%
ValueCountFrequency (%)
14 124
 
0.7%
13 70
 
0.4%
12 440
2.4%
11 127
 
0.7%
10 132
 
0.7%
9 422
2.3%
8 252
1.4%
7 234
1.3%
6.5 3
 
< 0.1%
6.3 1
 
< 0.1%
2025-02-02T22:15:40.843491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

iCodCondCielo
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.8 KiB
1
6482 
4
3485 
3
3447 
5
2809 
2
2067 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18290
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 6482
35.4%
4 3485
19.1%
3 3447
18.8%
5 2809
15.4%
2 2067
 
11.3%

Length

2025-02-02T22:15:42.407426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T22:15:42.640694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 6482
35.4%
4 3485
19.1%
3 3447
18.8%
5 2809
15.4%
2 2067
 
11.3%

Most occurring characters

ValueCountFrequency (%)
1 6482
35.4%
4 3485
19.1%
3 3447
18.8%
5 2809
15.4%
2 2067
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18290
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6482
35.4%
4 3485
19.1%
3 3447
18.8%
5 2809
15.4%
2 2067
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 18290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6482
35.4%
4 3485
19.1%
3 3447
18.8%
5 2809
15.4%
2 2067
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6482
35.4%
4 3485
19.1%
3 3447
18.8%
5 2809
15.4%
2 2067
 
11.3%

iCodDirViento
Numeric time series

Non stationary  Seasonal 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.354292
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:43.213593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median4
Q34
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.503676
Coefficient of variation (CV)0.34533192
Kurtosis0.84316034
Mean4.354292
Median Absolute Deviation (MAD)0
Skewness1.0144575
Sum79640
Variance2.2610415
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.988474166 × 10-23
2025-02-02T22:15:43.619514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
2025-02-02T22:15:44.840901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:45.189371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
4 11114
60.8%
3 2023
 
11.1%
6 1450
 
7.9%
8 1391
 
7.6%
2 1075
 
5.9%
7 828
 
4.5%
5 233
 
1.3%
1 176
 
1.0%
ValueCountFrequency (%)
1 176
 
1.0%
2 1075
 
5.9%
3 2023
 
11.1%
4 11114
60.8%
5 233
 
1.3%
6 1450
 
7.9%
7 828
 
4.5%
8 1391
 
7.6%
ValueCountFrequency (%)
8 1391
 
7.6%
7 828
 
4.5%
6 1450
 
7.9%
5 233
 
1.3%
4 11114
60.8%
3 2023
 
11.1%
2 1075
 
5.9%
1 176
 
1.0%
2025-02-02T22:15:43.972028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltVelocidadRafaga
Numeric time series

Non stationary  Seasonal 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.116293
Minimum4
Maximum74
Zeros0
Zeros (%)0.0%
Memory size285.8 KiB
2025-02-02T22:15:45.687431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q117
median22
Q332
95-th percentile44
Maximum74
Range70
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.70705
Coefficient of variation (CV)0.42629896
Kurtosis-0.19157783
Mean25.116293
Median Absolute Deviation (MAD)7
Skewness0.688877
Sum459377
Variance114.64091
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.314103792 × 10-19
2025-02-02T22:15:46.020214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-02-02T22:15:46.794307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:47.056159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
15 1760
 
9.6%
17 1406
 
7.7%
20 1329
 
7.3%
19 1319
 
7.2%
13 1265
 
6.9%
22 1030
 
5.6%
24 970
 
5.3%
30 890
 
4.9%
26 868
 
4.7%
28 801
 
4.4%
Other values (43) 6652
36.4%
ValueCountFrequency (%)
4 6
 
< 0.1%
5 3
 
< 0.1%
6 15
 
0.1%
7 63
 
0.3%
8 4
 
< 0.1%
9 258
 
1.4%
10 70
 
0.4%
11 738
4.0%
12 14
 
0.1%
13 1265
6.9%
ValueCountFrequency (%)
74 1
 
< 0.1%
67 4
 
< 0.1%
65 3
 
< 0.1%
63 8
 
< 0.1%
61 9
 
< 0.1%
59 14
 
0.1%
57 31
0.2%
56 41
0.2%
55 1
 
< 0.1%
54 75
0.4%
2025-02-02T22:15:46.301849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltPrecipitacion
Numeric time series

Skewed  Zeros 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0016894478
Minimum0
Maximum2.1
Zeros18241
Zeros (%)99.7%
Memory size285.8 KiB
2025-02-02T22:15:47.484886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.1
Range2.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.046671598
Coefficient of variation (CV)27.625357
Kurtosis1253.4688
Mean0.0016894478
Median Absolute Deviation (MAD)0
Skewness34.190378
Sum30.9
Variance0.0021782381
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.074961025 × 10-30
2025-02-02T22:15:47.811319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
2025-02-02T22:15:50.644917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:51.212439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 18241
99.7%
0.2 13
 
0.1%
0.1 12
 
0.1%
0.4 5
 
< 0.1%
1.4 3
 
< 0.1%
2 2
 
< 0.1%
1.3 2
 
< 0.1%
1.6 2
 
< 0.1%
1.2 2
 
< 0.1%
0.3 2
 
< 0.1%
Other values (6) 6
 
< 0.1%
ValueCountFrequency (%)
0 18241
99.7%
0.1 12
 
0.1%
0.2 13
 
0.1%
0.3 2
 
< 0.1%
0.4 5
 
< 0.1%
0.5 1
 
< 0.1%
0.8 1
 
< 0.1%
1.1 1
 
< 0.1%
1.2 2
 
< 0.1%
1.3 2
 
< 0.1%
ValueCountFrequency (%)
2.1 1
 
< 0.1%
2 2
< 0.1%
1.9 1
 
< 0.1%
1.7 1
 
< 0.1%
1.6 2
< 0.1%
1.4 3
< 0.1%
1.3 2
< 0.1%
1.2 2
< 0.1%
1.1 1
 
< 0.1%
0.8 1
 
< 0.1%
2025-02-02T22:15:48.592417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

fltDPT
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8381629
Minimum0
Maximum20
Zeros338
Zeros (%)1.8%
Memory size285.8 KiB
2025-02-02T22:15:51.787273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q314
95-th percentile16
Maximum20
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.8037258
Coefficient of variation (CV)0.48827467
Kurtosis-0.99551865
Mean9.8381629
Median Absolute Deviation (MAD)4
Skewness-0.3470345
Sum179940
Variance23.075781
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.298931067 × 10-7
2025-02-02T22:15:52.325726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
2025-02-02T22:15:53.834157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2025-02-02T22:15:54.366356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
15 1747
 
9.6%
14 1587
 
8.7%
16 1351
 
7.4%
13 1300
 
7.1%
11 1261
 
6.9%
12 1207
 
6.6%
10 1185
 
6.5%
9 1132
 
6.2%
8 974
 
5.3%
7 883
 
4.8%
Other values (11) 5663
31.0%
ValueCountFrequency (%)
0 338
 
1.8%
1 687
3.8%
2 707
3.9%
3 871
4.8%
4 713
3.9%
5 809
4.4%
6 850
4.6%
7 883
4.8%
8 974
5.3%
9 1132
6.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 20
 
0.1%
18 185
 
1.0%
17 482
 
2.6%
16 1351
7.4%
15 1747
9.6%
14 1587
8.7%
13 1300
7.1%
12 1207
6.6%
11 1261
6.9%
2025-02-02T22:15:52.879268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ACF and PACF

Interactions

2025-02-02T22:14:56.072856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:55.144644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:59.257600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:03.906182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:08.001099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:11.255198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:14.377981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:18.486975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:22.520876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:25.781395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:28.797224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:31.565258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:36.379656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:39.412265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:42.191094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:45.375731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:48.645757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:53.070893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:56.238705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:55.505471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:59.458693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:04.219656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:08.157318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:11.406789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:14.569918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:18.745273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:22.668319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:26.041764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:28.948786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:31.713566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:36.550815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:39.606516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:42.343070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:45.747917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:48.872936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:53.224308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:56.396699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:55.903396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:59.624363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:04.608469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:08.306636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:11.621695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:14.750337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:19.027811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:22.797796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:26.213797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:29.096322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:31.864983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:36.846633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:39.784069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:42.496122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:45.891427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:49.090292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:53.376495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:56.540100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:56.270755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:59.782326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:04.923913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:08.463044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:11.771082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:14.907971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:19.355182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:22.951438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:26.368016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:29.243938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:32.015070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:37.063457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:39.946176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:42.626254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:46.032212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:49.317378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:53.505729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:56.691037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:56.592780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:00.194663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:05.227191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:08.654045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:11.922619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:15.072726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:19.659355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:23.102216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:26.531336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:29.427811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:32.254313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:37.246768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:40.097151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:42.794602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:46.183266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:49.621277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:53.671990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:56.840335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:56.837256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:00.440094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:05.471902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:08.856722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:12.055067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:15.236592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:19.986860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:23.248067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:26.680498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:29.593899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:32.482544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:37.415530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:40.245605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:42.927429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:46.340084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:49.890193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:53.807722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:57.204083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:57.109010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:00.706038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:05.707010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:09.087622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:12.204183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:15.400659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:20.227851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:23.402770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:26.831358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:29.763271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:32.729384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:37.579471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:40.411927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:43.095929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:46.509303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:50.177656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:53.970958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:57.355026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:57.375276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:00.950692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:05.938803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:09.355790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:12.356251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:15.569031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:20.456352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:23.582579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:26.999597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:29.929998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:32.928190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:37.745433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:40.560041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:43.261622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:46.680444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:50.457103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:54.126197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:57.506574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:57.631914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:01.173466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:06.105961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:09.618775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:12.504271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:15.755905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:20.838115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:23.783939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:27.146764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:30.080475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:33.134380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:37.879753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:40.711464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:43.512399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:46.824864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:50.689939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:54.291523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:57.681734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:57.810068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:01.390412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:06.320400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:09.833777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:12.853405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:16.009964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:21.100437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:23.966583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:27.312322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:30.249520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:33.462929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:38.042820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:40.862506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:43.694668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:46.995536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:50.975395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:54.527309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:57.823672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:57.958277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:01.595431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:06.490289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:10.002733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:12.988014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:16.273113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:21.284493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:24.165970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:27.458362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:30.384315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:33.749556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:38.179536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:41.009572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:43.847414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:47.140816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:51.188628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:54.739500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:57.973225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:58.112673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:01.841699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:06.706273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:10.156148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:13.127465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:16.574285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:21.434860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:24.566083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:27.613653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:30.529752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:34.086194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:38.329097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:41.157758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:44.008291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:47.310833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:51.423355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:54.890205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:58.122158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:58.265493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:02.058377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:06.888115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:10.318204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:13.269582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:16.838873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:21.584794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:24.729586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:27.758481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:30.665020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:34.482848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:38.474244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:41.297152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:44.174348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:47.466473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:51.664104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:55.110093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:58.255422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:58.425529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:02.389706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:07.104958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:10.472092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:13.478798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:17.056645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:21.734668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:24.883508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:27.997941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:30.812051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:34.854943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:38.613796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:41.442753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:44.413397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:47.612448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:51.903598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:55.301982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:58.422530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:58.582488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:02.656564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:07.302109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:10.625900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:13.618777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:17.311072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:21.897571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:25.046517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:28.180148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:30.963602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:35.421748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:38.761095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:41.590713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:44.611816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:47.760134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:52.175822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:55.471068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:58.577667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:58.741220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:02.936765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:07.525289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:10.788160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:13.819408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:17.601524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:22.051548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:25.201190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:28.332390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:31.130011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:35.701785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:38.932872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:41.737232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:44.857691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:47.911841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:52.426862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:55.626343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:58.738567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:58.908077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:03.272001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:07.686452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:10.938896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:14.019119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:17.932013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:22.213928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:25.352432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:28.499341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:31.282697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:35.965773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:39.077466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:41.890810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:45.058483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:48.121810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:52.676725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:55.771507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:58.885987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:13:59.057521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:03.620073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:07.837461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:11.088328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:14.168140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:18.149822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:22.351444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:25.531578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:28.645097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:31.413395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:36.180315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:39.215664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:42.031759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:45.208046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:48.361699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:52.887977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-02T22:14:55.922535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Missing values

2025-02-02T22:14:59.119185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-02T22:14:59.639707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dtHorafltValPronosticoiNumDiaiNumAnioiNumSemanaiNumDiaSemanaiNumMesiCodDiaiCodHoraiNumHorafltTempfltProbabilidadLluviafltHumedadRelativafltVelocidadVientofltCoberturaNubesfltIndiceUViCodCondCieloiCodDirVientofltVelocidadRafagafltPrecipitacionfltDPT
2022-09-01 00:00:002022-09-01 00:00:000.0000001202236596452154847241977715980.044320.015
2022-09-01 01:00:002022-09-01 01:00:000.000000120223659645315484811978213820.044320.016
2022-09-01 02:00:002022-09-01 02:00:000.000000120223659645315484921898511730.034280.016
2022-09-01 03:00:002022-09-01 03:00:000.0000001202236596453154850318118711630.034260.016
2022-09-01 04:00:002022-09-01 04:00:000.0000001202236596453154851418118811700.034240.016
2022-09-01 05:00:002022-09-01 05:00:000.0000001202236596453154852517158611450.034220.014
2022-09-01 06:00:002022-09-01 06:00:000.0000001202236596453154853618478911540.034220.016
2022-09-01 07:00:002022-09-01 07:00:006.584959120223659645315485471851959700.044220.017
2022-09-01 08:00:002022-09-01 08:00:00560.4220221202236596453154855818471009720.034220.018
2022-09-01 09:00:002022-09-01 09:00:007720.582326120223659645315485691851009811.044220.018
dtHorafltValPronosticoiNumDiaiNumAnioiNumSemanaiNumDiaSemanaiNumMesiCodDiaiCodHoraiNumHorafltTempfltProbabilidadLluviafltHumedadRelativafltVelocidadVientofltCoberturaNubesfltIndiceUViCodCondCieloiCodDirVientofltVelocidadRafagafltPrecipitacionfltDPT
2024-10-02 15:00:002024-10-02 15:00:0025562.022024404107215173151152503413435.033240.08
2024-10-02 16:00:002024-10-02 16:00:0025386.022024404107215173152162603113254.023240.08
2024-10-02 17:00:002024-10-02 17:00:0022872.022024404107215173153172603215182.014280.08
2024-10-02 18:00:002024-10-02 18:00:0015825.022024404107215173154182503317111.014320.08
2024-10-02 19:00:002024-10-02 19:00:001450.02202440410721517315519230381930.014370.08
2024-10-02 20:00:002024-10-02 20:00:000.02202440410721517315620220452020.014370.09
2024-10-02 21:00:002024-10-02 21:00:000.02202440410721517315721200541910.014390.010
2024-10-02 22:00:002024-10-02 22:00:000.02202440410721517315822180621900.014410.011
2024-10-02 23:00:002024-10-02 23:00:000.02202440410721517315923170691500.014320.011
2024-10-03 00:00:002024-10-03 00:00:000.0320244051072151731602416073900.014220.011